Evidence map›Paper›PMID 41492957›Full record

ArticleGenetic epidemiology2026

Archipelago Method for Variant Set Association Test Statistics.

Dylan Lawless, Ali Saadat, Mariam Ait Oumelloul, Luregn J Schlapbach, Jacques Fellay

Abstract read
In one paragraph

Article in Genetic epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Dylan LawlessDepartment of Intensive Care and Neonatology, University Children's Hospital Zürich, University of Zürich, Zürich, Switzerland.ORCID https://orcid.org/0000-0001-8496-3725
Ali SaadatGlobalHealth Institute, School of Life Sciences, ÉcolePolytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Mariam Ait OumelloulGlobalHealth Institute, School of Life Sciences, ÉcolePolytechnique Fédérale de Lausanne, Lausanne, Switzerland.
Luregn J SchlapbachDepartment of Intensive Care and Neonatology, University Children's Hospital Zürich, University of Zürich, Zürich, Switzerland.
Jacques FellayGlobalHealth Institute, School of Life Sciences, ÉcolePolytechnique Fédérale de Lausanne, Lausanne, Switzerland.

Funding

The Swiss Pediatric Sepsis Study received funding from the Swiss National Science Foundation 320030_201060/1The Swiss Personalized Health Network and the Strategic Focal Area 'Personalized Health and Related Technologies' of the ETH Domain (Swiss Federal Institutes of Technology) NDS-2021-911
6 · The paper itself

Abstract

Variant set association tests (VSAT), especially those incorporating rare variants via variant collapse, are invaluable in genetic studies. However, unlike Manhattan plots for single-variant tests, VSAT statistics lack intrinsic genomic coordinates, hindering visual interpretation. To overcome this, we developed the Archipelago method, which assigns a meaningful genomic coordinate to VSAT P values so that both set-level and individual variant associations can be visualised together. This results in an intuitive and information rich illustration akin to an Archipelago of clustered islands, enhancing the understanding of both collective and individual impacts of variants. We conducted three validation studies spanning simulated and real datasets across small and biobank-scale cohorts, from 504 individuals up to 490,640 UK Biobank participants. We integrated single-variant genome-wide association studies (GWAS) with gene- and protein pathway-level rare-variant collapse. These studies included the 1KG GWAS cohort, the Pan-UK Biobank GWAS with DeepRVAT WES gene-level study, and the UKBB WGS gene-level UTR collapsing PheWAS. The Archipelago plot is applicable in any genetic association study that uses variant collapse to evaluate both individual variants and variant sets, and its customisability facilitates clear communication of complex genetic data. By integrating at least two dimensions of genetic data into a single visualisation, VSAT results can be easily read and aid in identification of potential causal variants in variant sets such as protein pathways.

Indexed as

Genome-Wide Association StudyBiological Specimen BanksComputer SimulationGenetic VariationHumansPolymorphism, Single NucleotideUnited KingdomArchipelagocombinedGWASRVATVSAT

Identifiers

PMID41492957
PMCPMC12771271

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.